Extrinsic vs Intrinsic Criticality in Systems with Many Components

TL;DR

This paper distinguishes between intrinsic and extrinsic criticality in complex systems, linking them to learning and inference, and explores their observable signatures like Zipf's law, challenging the notion that fine tuning is necessary for criticality.

Contribution

It unifies intrinsic and extrinsic criticality through information theory, revealing their continuum and implications for understanding biological systems without fine tuning.

Findings

Critical correlations lead to diverging mutual information.

Zipf's law is robust in extrinsic criticality but less so in intrinsic cases.

Global dynamics can produce Zipf's law without fine tuning.

Abstract

Biological systems with many components often exhibit seemingly critical behaviors, characterized by atypically large correlated fluctuations. Yet the underlying causes remain unclear. Here we define and examine two types of criticality. Intrinsic criticality arises from interactions within the system which are fine-tuned to a critical point. Extrinsic criticality, in contrast, emerges without fine tuning when observable degrees of freedom are coupled to unobserved fluctuating variables. We unify both types of criticality using the language of learning and information theory. We show that critical correlations, intrinsic or extrinsic, lead to diverging mutual information between two halves of the system, and are a feature of learning problems, in which the unobserved fluctuations are inferred from the observable degrees of freedom. We argue that extrinsic criticality is equivalent to standard inference, whereas intrinsic criticality describes fractional learning, in which the amount to be learned depends on the system size. We show further that both types of criticality are on the same continuum, connected by a smooth crossover. In addition, we investigate the observability of Zipf's law, a power-law rank-frequency distribution often used as an empirical signature of criticality. We find that Zipf's law is a robust feature of extrinsic criticality but can be nontrivial to observe for some intrinsically critical systems, including critical mean-field models. We further demonstrate that models with global dynamics, such as oscillatory models, can produce observable Zipf's law without relying on either external fluctuations or fine tuning. Our findings suggest that while possible in theory, fine tuning is not the only, nor the most likely, explanation for the apparent ubiquity of criticality in biological systems with many components.